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Patient-specific air puff-induced loading using machine learning.

Nada A Desouky¹, Mahmoud M Saafan², Mohamed H Mansour¹

¹Mechanical Power Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt.

Frontiers in Bioengineering and Biotechnology

|November 29, 2023

Summary

This summary is machine-generated.

This study introduces a machine learning algorithm to accurately predict air puff pressure distribution for intraocular pressure (IOP) measurements. The method significantly reduces computation time, improving diagnostic accuracy for corneal diseases.

Keywords:

Gradient Boosting Regressor (GBR)air puff pressure fluid-structure interaction (FSI)intraocular pressure (IOP)machine learning (ML)ocular biomechanics reduced order modelling

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Area of Science:

Ophthalmology
Biomedical Engineering
Computational Fluid Dynamics
Machine Learning

Background:

The air puff test is a contactless method for measuring intraocular pressure (IOP) and corneal biomechanical properties.
Inaccuracies in IOP estimation arise from the correlation between IOP and corneal parameters (material and geometry).
Current fluid-structure interaction (FSI) models are computationally intensive, limiting clinical application.

Purpose of the Study:

To develop a machine learning algorithm for predicting patient-specific air puff pressure distribution.
To reduce the computational time of FSI simulations for air puff tests.
To improve the accuracy of intraocular pressure (IOP) and corneal material stress-strain index (SSI) measurements.

Main Methods:

A supervised machine learning algorithm, specifically gradient boosting, was employed.
The algorithm was trained on a parametric study of corneal deformations and air puff pressure distribution.
The predicted pressure distribution was applied to a finite element eye model incorporating FSI.

Main Results:

The machine learning algorithm accurately predicted time-dependent air puff pressure distribution (MAE: 0.0258, RMSE: 0.0673).
Computational time was drastically reduced from ~28 hours to 12 minutes (99.2% reduction).
The model generated corneal deformations considering FSI, enabling extraction of response parameters for improved IOP and SSI algorithms.

Conclusions:

Accurate estimation of air puff pressure distribution is crucial for precise IOP measurements and corneal disease detection.
The developed ML algorithm preserves the accuracy of CFD-based FSI models while significantly reducing computational cost.
This advancement facilitates more accurate parametric equations for IOP and SSI, benefiting clinical practice and research.